Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.publishedVersio

BASIC ALGORITHM FOR INDUCTION MOTORS ROTOR FAULTS PRE-DETERMINATION

Due to importance of squirrel cage induction motor in today’s industry, the fault detection on that type of motors has become a highly developed area of interest for researchers. The electrical machine is designed for stable operations with minimum noise and vibrations under the normal conditions. When the fault emerges, some additional distortions appear. The necessity to detect the fault in an early stage, to prevent further damage of the equipment due to fault propagation, is one of the most important features of any condition monitoring or diagnostic techniques for electrical machines nowadays. In this paper possible induction motors faults classified and basic algorithm for rotor faults pre-determination is presented

Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical Machines

Industrial revolution 4.0 has enabled the advent of new technological advancements, including the introduction of information technology with physical devices. The implementation of information technology in industrial applications has helped streamline industrial processes and make them more cost-efficient. This combination of information technology and physical devices gave birth to smart devices, which opened up a new research area known as the Internet of Things (IoT). This has enabled researchers to help reduce downtime and maintenance costs by applying condition monitoring on electrical machines utilizing machine learning algorithms. Although the industry is trying to move from scheduled maintenance towards predictive maintenance, there is a significant lack of algorithms related to fault prediction of electrical machines. There is quite a lot of research going on in this area, but it is still underdeveloped and needs a lot more work. This paper presents a signal spectrum-based machine learning approach toward the fault prediction of electrical machines. The proposed method is a new approach to the predictive maintenance of electrical machines. This paper presents the details regarding the algorithm and then validates the accuracy against data collected from working electrical machines for both cases. A comparison is also presented at the end of multiple machine learning algorithms used for training based on this approach.Signal Spectrum-Based Machine Learning Approach for Fault Prediction and Maintenance of Electrical MachinespublishedVersio

Design and Prototyping of Directly Driven Outer Rotor Permanent Magnet Generator for Small Scale Wind Turbines

The paper is about the design and prototyping of directly driven outer rotor permanent magnet generator for small scale wind turbine. In the paper, the initial design of the generator is given. Main issues and phenomena affecting the generator design, such as cogging torque and its reduction possibilities, selection and demagnetization risk assessment of permanent magnets, machine losses and thermal analysis, are described. Test results of prototype generator construction and final parameters are also presented. The necessity of further study is pointed out

Implementation of Digital Twins for electrical energy conversion systems in selected case studies

Reference implementation of Digital Twins for electrical energy conversion systems is an important and open question in the industrial domain. Digital Twins can predict the future performance, behaviour, and maintenance needs of a complex system. Today the concept of Digital Twins is not only an emulation or simulation of the physical object along with its development history but also contains much information from the respective manufacturers and services. This paper presents the current state­of­the­art of Digital Twins in relation to some interesting novel applications from different fields of electrical engineering. The objective of the paper is to give an overview of the successful application of Digital Twins in electrical energy conversion systems, such as industrial robotics and wind turbines; to discuss trends in applications like electric vehicles; and to suggest new applications, such as telescopes. Special attention is paid to the possible application of Digital Twins in faults diagnostics and prognostics of electrical energy conversion systems. Successful implementation of Digital Twins in any electrical energy conversion system diagnostics and prognostics allows for low­cost maintenance, higher utilization of the individual devices and systems, as well as lower usage of material and human resources. A SWOT analysis was performed for Digital Twin applications in electrical energy conversion systems. The latter is a useful analysis technique that explores possibilities for new achievements or solutions to existing problems and makes decisions about the best path

Additive Manufacturing of Electrical Machines—Towards the Industrial Use of a Novel Technology

For several decades, the design and manufacture of electrical machines has been considered a technically mature area and, as a result, research and development in the area has been extremely limited, even though this is a crucial technology in the application of electrical energy. Electrical machines are used in over 80% of the world’s energy conversion processes—first to create electrical energy, which can be easily transmitted, and second to convert that energy into mechanical form for applications ranging from dishwashers to transportation, and from medical devices to those used for industrial processes. Today, two technologies are changing this. The first is the development of power electronic drives and the second is the introduction of additive manufacturing technology. The latter technology has opened up new areas for innovation and research, and many conventional processes are likely to become obsolete. Considering the overall consumption of electricity by electrical machines, the design freedom granted by the novel production technology gives the opportunity for even more efficient, object-oriented machines to be built, with a lower environmental impact and less raw material consumption. If this technology can be developed to maturity, it would have a significant positive impact on the desired green transition that is being pursued all over the world

Condition Monitoring of Electrical Machines

Abstract-All machines, no matter how well they are designed, calculated or produced have the tendency to fail at some point of their existence. If the condition of the machines in use is not monitored in any way and the maintenance is forgotten, the resulting failures may pose a large economic and safety risk. Even the small and at first not important faults can end in catastrophic measures. This paper describes the main faults that occur in electrical machines and the diagnostic methods that can be used to detect these faults

Recent Trends in Additive Manufacturing and Topology Optimization of Reluctance Machines

Additive manufacturing (AM) or 3D printing has opened up new opportunities for researchers in the field of electrical machines, as it allows for more flexibility in design and faster prototyping, which can lead to more efficient and cost-effective production. An overview of the primary AM techniques utilized for designing electrical machines is presented in this paper. AM enables the creation of complex and intricate designs that are difficult or impossible to achieve using traditional methods. Topology Optimization (TO) can be used to optimize the design of parts for various purposes such as weight, thermal, material usage and structural performance. This paper primarily concentrates on the most recent studies of the AM and TO of the reluctance machines. The integration of AM with TO can enhance the design and fabrication process of magnetic components in electrical machines by overcoming current manufacturing limitations and enabling the exploration of new design possibilities. The technology of AM and TO both have limitations and challenges which are discussed in this paper. Overall, the paper offers a valuable resource for researchers and practitioners working in the field of AM and TO of electrical machines